R Help converting factor data from long to wide and assigning logical value

问题

I have data in long format as seen below:

Data:

id code
1  EP
2  EP
3  EP
4  UM
5  UM
1  UM
2  UM
10 UM
6  BZ
7  BZ
14 BZ
2  BZ
8  TVOL
9  TVOL
16 TVOL
10 NW
11 NW
7  NW
12 SM
13 SM
3  SM
14 GS
15 GS
1  GS
2  GS
9  GS

I would like to create a wide dataframe with each "code" as its own column marked TRUE/FALSE depending on whether there's an associated "id" as seen in the minimal example below:

id code.EP code.UM code.BZ code.TVOL code.NW code.SM code.GS
1  TRUE    TRUE    FALSE   FALSE     FALSE   FALSE   TRUE
2  TRUE    FALSE   TRUE    FALSE     FALSE   FALSE   TRUE
3  TRUE    FALSE   FALSE   FALSE     FALSE   TRUE    FALSE
4  FALSE   TRUE    FALSE   FALSE     FALSE   FALSE   FALSE
5  FALSE   TRUE    FALSE   FALSE     FALSE   FALSE   FALSE

Apologies if this has been answered before (I'm positive it is in various forms), I just can't seem to understand the similar examples I've found.

回答1:

We can use table and convert to logical

table(df1) > 0
# code
#id      BZ    EP    GS    NW    SM  TVOL    UM
#  1  FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#  2   TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#  3  FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#  4  FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#  5  FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#  6   TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#  7   TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#  8  FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#  9  FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#  10 FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#  11 FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#  12 FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#  13 FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#  14  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#  15 FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#  16 FALSE FALSE FALSE FALSE FALSE  TRUE FALSE

data

df1 <- structure(list(id = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 10L, 6L, 7L, 
14L, 2L, 8L, 9L, 16L, 10L, 11L, 7L, 12L, 13L, 3L, 14L, 15L, 1L, 
2L, 9L), code = c("EP", "EP", "EP", "UM", "UM", "UM", "UM", "UM", 
"BZ", "BZ", "BZ", "BZ", "TVOL", "TVOL", "TVOL", "NW", "NW", "NW", 
"SM", "SM", "SM", "GS", "GS", "GS", "GS", "GS")), 
class = "data.frame", row.names = c(NA, 
-26L))

回答2:

For a tidyverse approach try this:

library(dplyr)
library(tidyr)

df <- structure(list(id = c(
  "1", "2", "3", "4", "5", "1", "2", "10",
  "6", "7", "14", "2", "8", "9", "16", "10", "11", "7", "12", "13",
  "3", "14", "15", "1", "2", "9"
), code = c(
  "EP", "EP", "EP", "UM",
  "UM", "UM", "UM", "UM", "BZ", "BZ", "BZ", "BZ", "TVOL", "TVOL",
  "TVOL", "NW", "NW", "NW", "SM", "SM", "SM", "GS", "GS", "GS",
  "GS", "GS"
)), row.names = c(NA, -26L), class = c(
  "tbl_df", "tbl",
  "data.frame"
))

df %>%
  pivot_wider(id_cols = "id", names_prefix = "code.", names_from = "code", values_from = "code") %>% 
  mutate_at(vars(starts_with("code")), ~ ifelse(!is.na(.x), TRUE, FALSE)) 
#> # A tibble: 16 x 8
#>    id    code.EP code.UM code.BZ code.TVOL code.NW code.SM code.GS
#>    <chr> <lgl>   <lgl>   <lgl>   <lgl>     <lgl>   <lgl>   <lgl>  
#>  1 1     TRUE    TRUE    FALSE   FALSE     FALSE   FALSE   TRUE   
#>  2 2     TRUE    TRUE    TRUE    FALSE     FALSE   FALSE   TRUE   
#>  3 3     TRUE    FALSE   FALSE   FALSE     FALSE   TRUE    FALSE  
#>  4 4     FALSE   TRUE    FALSE   FALSE     FALSE   FALSE   FALSE  
#>  5 5     FALSE   TRUE    FALSE   FALSE     FALSE   FALSE   FALSE  
#>  6 10    FALSE   TRUE    FALSE   FALSE     TRUE    FALSE   FALSE  
#>  7 6     FALSE   FALSE   TRUE    FALSE     FALSE   FALSE   FALSE  
#>  8 7     FALSE   FALSE   TRUE    FALSE     TRUE    FALSE   FALSE  
#>  9 14    FALSE   FALSE   TRUE    FALSE     FALSE   FALSE   TRUE   
#> 10 8     FALSE   FALSE   FALSE   TRUE      FALSE   FALSE   FALSE  
#> 11 9     FALSE   FALSE   FALSE   TRUE      FALSE   FALSE   TRUE   
#> 12 16    FALSE   FALSE   FALSE   TRUE      FALSE   FALSE   FALSE  
#> 13 11    FALSE   FALSE   FALSE   FALSE     TRUE    FALSE   FALSE  
#> 14 12    FALSE   FALSE   FALSE   FALSE     FALSE   TRUE    FALSE  
#> 15 13    FALSE   FALSE   FALSE   FALSE     FALSE   TRUE    FALSE  
#> 16 15    FALSE   FALSE   FALSE   FALSE     FALSE   FALSE   TRUE

回答3:

additional option

library(tidyverse)
df %>%
  mutate(index = T) %>% 
  pivot_wider(id, names_from = code, values_from = index, names_prefix = "code.") %>% 
  mutate_all(replace_na, F)

来源：https://stackoverflow.com/questions/60744395/r-help-converting-factor-data-from-long-to-wide-and-assigning-logical-value

标签

reshape

data-wrangling